Rothschild Lecture: From Small Data to Big Data and Back: Statistics and Data Science
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SNA  Theoretical foundations for statistical network analysis
Modern statistics began with R.A.Fisher’s seminal work in the early twentieth century, with important predecessors such as Karl Pearson, contemporaries such as J.Neyman and successors such as A. Wald. The focus was on small data sets or summaries of larger ones. Analyses were based on simple models and given in terms of estimates, testing and confidence bounds. With the advent of big data, the size of datasets, their complexity and heterogeneity, and the lack of theory to build mechanistic probability models brought new issues to the fore. I will discuss some of these issues: 1. Computation 2. Prediction 3. Sparsity/dimension reduction4. Stability/robustness 5. Reduction to small data
This talk is part of the Isaac Newton Institute Seminar Series series.
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